Trina M. Norden-Krichmar

Cirrhosis and hepatocellular carcinoma (HCC) are long-term complications of chronic liver disease (CLD). In this large multi-ancestry genome-wide association study of all-cause cirrhosis (35,481 cases, 2.36M controls) and HCC (6,680 cases, 1.76M controls), we identified 27 loci associated with cirrhosis (10 novel) and 11 with HCC (three novel). Three novel cirrhosis loci were replicated in independent cohorts (e.g. FGF21, RPTOR, and IFNL3/4). Fifteen cirrhosis loci exhibited differential effects on cirrhosis risk via underlying etiologies, and six HCC loci influenced HCC risk indirectly via cirrhosis. In a gene-burden analysis of rare variants from whole-genome sequencing data in the VA Million Veteran Program (n=102,677), we identified GSTA5 as a novel cirrhosis-associated gene, while APOB and ATP9B were associated with and replicated for HCC. A high genetic risk score for cirrhosis was associated with a nearly doubled risk of CLD progressing to cirrhosis (HR=1.94, P=2×10⁻⁶⁸) and of cirrhosis progressing to HCC (HR=1.65, P=7×10⁻⁰⁸). Finally, among individuals with chronic hepatitis C who underwent antiviral therapy, cirrhosis risk was modified by variants in PNPLA3, IFNL3/4, and CD81 following pegylated interferon-α therapy, and by APOE lead variant following direct-acting antiviral therapy. These findings provide new insights into the complex genetic architecture of CLD progression with potential clinical and therapeutic implications.

Background: Fibrosis-4 (FIB4) is a recommended noninvasive test to assess hepatic fibrosis among patients with metabolic dysfunction–associated steatotic liver disease (MASLD). Here, we used FIB4 trajectory over time (ie, “slope” of FIB4) as a surrogate marker of liver fibrosis progression and examined if FIB4 slope is associated with clinical and genetic factors among individuals with clinically defined MASLD within the Million Veteran Program Cohort. Methods: In this retrospective cohort study, FIB4 slopes were estimated through linear regression for participants with clinically defined MASLD and FIB4 p < 0.05). FIB4 slopes also correlated strongly with hepatic outcomes and were independently associated with time to cirrhosis. Five genetic loci showed genome-wide significant associations (p < 5 × 10⁻⁸) with FIB4 slope among European ancestry subjects, including 2 known (PNPLA3 and TM6SF2) and 3 novel loci (TERT 5.1 × 10⁻¹¹; LINC01088, 3.9 × 10⁻⁸; and MRC1, 2.9 × 10⁻⁹). Conclusions: Linear trajectories of FIB4 correlated significantly with time to progression to cirrhosis, with liver-related outcomes among individuals with MASLD and with known and novel genetic loci. FIB4 slope may be useful as a surrogate measure of fibrosis progression.

Distinguishing between alcohol-associated hepatitis (AH) and alcohol-associated cirrhosis (AC) remains a diagnostic challenge. In this study, we used machine learning with transcriptomics and proteomics data from liver tissue and peripheral mononuclear blood cells (PBMCs) to classify patients with alcohol-associated liver disease. The conditions in the study were AH, AC, and healthy controls. We processed 98 PBMC RNAseq samples, 55 PBMC proteomic samples, 48 liver RNAseq samples, and 53 liver proteomic samples. First, we built separate classification and feature selection pipelines for transcriptomics and proteomics data. The liver tissue models were validated in independent liver tissue datasets. Next, we built integrated gene and protein expression models that allowed us to identify combined gene-protein biomarker panels. For liver tissue, we attained 90% nested-cross validation accuracy in our dataset and 82% accuracy in the independent validation dataset using transcriptomic data. We attained 100% nested-cross validation accuracy in our dataset and 61% accuracy in the independent validation dataset using proteomic data. For PBMCs, we attained 83% and 89% accuracy with transcriptomic and proteomic data, respectively. The integration of the two data types resulted in improved classification accuracy for PBMCs, but not liver tissue. We also identified the following gene-protein matches within the gene-protein biomarker panels: CLEC4M-CLC4M, GSTA1-GSTA2 for liver tissue and SELENBP1-SBP1 for PBMCs. In this study, machine learning models had high classification accuracy for both transcriptomics and proteomics data, across liver tissue and PBMCs. The integration of transcriptomics and proteomics into a multi-omics model yielded improvement in classification accuracy for the PBMC data. The set of integrated gene-protein biomarkers for PBMCs show promise toward developing a liquid biopsy for alcohol-associated liver disease.

IPSC reprogramming involves dynamic changes in chromatin accessibility necessary for the conversion of somatic cells into induced pluripotent stem cells (iPSCs). IPSCs can be used to generate a wide range of cells to potentially replace damaged cells in a patient without the threat of immune rejection; however, efficiently reprogramming cells for medical applications remains a challenge, particularly in human cells. Here, we conducted a cross-species meta-analysis to identify conserved and species-specific differences in regulatory patterns during reprogramming. Chromatin accessibility and transcriptional data as fibroblasts transitioned to iPSCs were obtained from the publicly available Gene Expression Omnibus (GEO) database and integrated to generate time-resolved regulatory networks during cellular reprogramming. We observed consistent and conserved trends between the species in the chromatin accessibility signatures as cells transitioned from fibroblasts into iPSCs, indicating distal control of genes associated with pluripotency by master reprogramming regulators. Multi-omic integration showed key network changes across reprogramming states, revealing regulatory relationships between chromatin regulators, enhancers, transcription factors, and target genes that result in the silencing of the somatic transcription program and activation of the pluripotency gene regulatory network. This integrative analysis revealed distinct network changes between timepoints and leveraged multi-omics to gain novel insights into the regulatory mechanisms underlying reprogramming.

Objective: Metabolic syndrome (MetS) is defined by clustering of cardiometabolic components, which may be present in different combinations. The authors evaluated clustering in individuals and extended families within and across ancestry groups.Methods: The prevalence of different combinations of MetS components (high fasting glucose, low high‐density lipoprotein cholesterol, high triglycerides, high blood pressure, and abdominal obesity) was estimated in 1651 individuals (340 families) self‐reporting as European American (EA), Hispanic/Mexican American (MA), African American (AA), and Japanese American (JA). Odds ratios were estimated using logistic regression with generalized estimating equations comparing individual MetS components, number, and combinations of components for each ancestry group versus EA.Results: Clustering of all five components (Combination #16) was more prevalent in EA (29.9%) and MA (25.2%) individuals than in AA (18.7%) and JA (15.5%) individuals. Compared with EA individuals, AA individuals were 64% and 66% less likely to have high triglycerides and low high‐density lipoprotein cholesterol, whereas JA individuals were 85% and 56% less likely to have abdominal obesity and high blood pressure, respectively. Compared with EA individuals, the odds of having two, four, or five components were at least 77% lower in JA individuals, whereas the odds of having three, four, or five components were at least 3.79 times greater in MA individuals.Conclusions: Understanding heterogeneity in MetS clustering may identify factors important in reducing health disparities.

Excessive alcohol use is a major risk factor for the development of an alcohol use disorder (AUD) and contributes to a wide variety of other medical illnesses, including alcohol-associated liver disease (ALD). Both AUD and ALD are complex and causally interrelated diseases, and multiple factors other than alcohol consumption are implicated in the disease pathogenesis. While the underlying pathophysiology of AUD and ALD is complex, there is substantial evidence for a genetic susceptibility of both diseases. Current genome-wide association studies indicate that the genes associated with clinical AUD only poorly overlap with the genes identified for heavy drinking and, in turn, neither overlap with the genes identified for ALD. Uncovering the main genetic factors will enable us to identify molecular drivers underlying the pathogenesis, discover potential targets for therapy, and implement patient care early in disease progression. In this review, we described multiple genomic approaches and their implications to investigate the susceptibility and pathogenesis of both AUD and ALD. We concluded our review with a discussion of the knowledge gaps and future research on genomic studies in these 2 diseases.

Environmental Health Perspectives is an Open Access journal published by the National Institute of Environmental Health Sciences. Environmental Health Perspectives is an Open Access journal published by the National Institute of Environmental Health Sciences.